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Brainstorm Report: Schedule Red-Team Stress Test for Intent Parser Edge Cases

June 28, 2026

artifact_id: content-draft-81958b8b-76f9-48b4-ae57-1780476b49fc source_session: 8a5ec7dd-618c-4b70-806f-c846db89d696 version: v01 audience: review board publish_target: content pipeline content_type: report title: "Brainstorm Report: Schedule Red-Team Stress Test for Intent Parser Edge Cases" reviewer_ask: Review for factual grounding, usefulness, publication readiness, and required revisions.

Brainstorm Report: Schedule Red-Team Stress Test for Intent Parser Edge Cases

Summary
This session focused on designing a red-team stress test to expose edge cases in the intent parser, prioritizing scenarios that challenge the system’s ability to handle ambiguity, cultural specificity, and asynchronous input. Key outcomes include the creation of an audit evidence table, prioritization of three high-severity audit gaps, and scheduling of synthetic testing with dialects, sarcasm, and fragmented inputs. The discussion also flagged computational resource constraints for adversarial input generation.

Context

The intent parser, a core component of the system, must handle complex, ambiguous, or culturally specific user inputs. However, gaps in its resilience to edge cases—such as contradictory subgoals, sarcasm, or fragmented input—remain untested. The red-team stress test aims to surface these weaknesses by simulating adversarial scenarios that push the parser beyond its training data.

Key Discussion Points

  1. Audit Evidence Table as a Prerequisite
    Subrosa vetoed initial proposals, emphasizing the need for a structured audit evidence table (/workspace/projects/subcorp/audit/evidence_table.md) to map test scenarios to existing audit gaps. This table would track severity, test cases, and fallback validation triggers (e.g., human escalation).

  2. Test Scenarios

    • Sarcasm and Dialects: Thaum proposed testing inputs like “I’m not late, I’m just fashionably inconvenienced” to see if the parser detects sarcasm and escalates to human review. Praxis expanded this to include synthetic prompts in African American Vernacular English (AAVE), Yoda speak, and sarcastic phrasing.
    • Asynchronous/Fragmented Input: Thaum suggested testing scenarios like “Book a flight to [pause for 30 seconds]” to evaluate how the system handles incomplete or delayed input.
    • Adversarial Inputs: Chora proposed using GANs to generate inputs exploiting parser failure modes, though this requires significant computational resources.

  3. Prioritization of Audit Gaps
    The team agreed to prioritize three high-severity gaps:

    • Sarcasm detection (does the parser flag ambiguous intent?).
    • Dialect-specific parsing (does the system handle culturally specific phrasing?).
    • Asynchronous input handling (does the system degrade gracefully or escalate?).

  4. Resource Constraints
    Chora noted that GAN-generated adversarial inputs, while high-value, demand computational power and may delay testing. The team opted to proceed with sarcasm/dialect testing first, deferring adversarial input generation to a later phase.

Decisions

  • Audit Table Creation: Praxis created /workspace/projects/subcorp/audit/evidence_table.md with high-severity entries for sarcasm, dialect-specific parsing, and asynchronous input handling.
  • Test Scenario Mapping: Each audit gap was mapped to specific test cases (e.g., “I’m not late…” for sarcasm, AAVE/Yoda prompts for dialects, and fragmented input for asynchronous handling).
  • Red-Team Stress Test Schedule: Praxis scheduled the test to prioritize synthetic prompts in AAVE, Yoda speak, and sarcastic phrasing, measuring fallback validation rates.

Action Items

  1. Complete Audit Evidence Table

    • [Praxis] Finalize entries for sarcasm, dialect-specific parsing, and asynchronous input handling, ensuring alignment with existing audit gaps.

  2. Prepare Synthetic Prompts

    • [Thaum/Praxis] Generate 500+ prompts in AAVE, Yoda speak, and sarcastic phrasing.

  3. Implement Red-Team Test

    • [Praxis] Execute stress test using synthetic prompts, logging fallback escalation rates and parser failure modes.

  4. Resource Allocation for Adversarial Inputs

    • [Chora] Assess computational feasibility of GAN-generated adversarial inputs; propose a phased approach if resources are limited.

Next Steps

  • Week 1: Finalize audit table and synthetic prompt generation.
  • Week 2: Execute red-team stress test, focusing on sarcasm/dialect scenarios.
  • Week 3: Analyze results, escalate findings to human review where applicable, and plan adversarial input testing.

Artifact Path: /workspace/output/reports/2026-06-28__brainstorm__report__schedule-red-team-stress-test-for-intent__thaum__v01.md

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